HelpingAI2 9B i1 needs ~10.0 GB VRAM. MacBook Pro M3 24GB has 17.3 GB. With Q4_K_M quantization, expect ~12 tok/s.
Operating mode
Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.
Current mode
Balanced
Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.
Select quantization to explore
Fit status
Runs well
Decode
12.4 tok/s
TTFT
15630 ms
Safe context
126K
Memory
10.0 GB / 17.3 GB
This setup is broadly balanced for this model.
Shared-memory contention still exists
The OS, browser, and inference runtime all compete for the same physical memory pool, so real-world headroom is less forgiving than raw capacity suggests.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 12.4 tok/s | 8526 ms | 126K |
| Coding | C | Runs well | 12.4 tok/s | 15630 ms | 126K |
| Agentic Coding | C | Runs well | 12.4 tok/s | 22735 ms | 126K |
| Reasoning | C | Runs well | 12.4 tok/s | 18472 ms | 126K |
| RAG | C | Runs well | 12.4 tok/s | 28419 ms | 126K |
How HelpingAI2 9B i1 (9B params) fits at each quantization level on MacBook Pro M3 24GB (17.3 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.5 GB | Low | C46 |
Q3_K_S | 3 | 4.4 GB | Low | C47 |
NVFP4 | 4 | 5.0 GB | Medium | C48 |
Q4_K_M | 4 | 5.5 GB | Medium | C48 |
Q5_K_M | 5 | 6.5 GB | High | C49 |
Q6_K | 6 | 7.4 GB | High | C50 |
Q8_0Best for your GPU | 8 | 9.6 GB | Very High | C51 |
F16 | 16 | 18.5 GB | Maximum | F0 |
Copy-paste commands to run HelpingAI2 9B i1 on your machine.
Run
lms load hf-mradermacher--helpingai2-9b-i1-gguf && lms server startUpgrade-Optionen
Raises estimated decode speed by about 241%.
Adds memory headroom for longer context windows and future model growth.
ca. $1,999 MSRP
Raises estimated decode speed by about 106%.
Adds memory headroom for longer context windows and future model growth.
ca. $1,999 MSRP
Raises estimated decode speed by about 223%.
Adds memory headroom for longer context windows and future model growth.
ca. $2,499 MSRP
Yes, MacBook Pro M3 24GB can run HelpingAI2 9B i1 with a C grade (Runs well). Expected decode speed: 12.4 tok/s.
HelpingAI2 9B i1 (9B parameters) requires approximately 10.0 GB of memory with Q4_K_M quantization.
The recommended quantization for HelpingAI2 9B i1 is Q4_K_M, which balances quality and memory efficiency.
On MacBook Pro M3 24GB, HelpingAI2 9B i1 achieves approximately 12.4 tokens per second decode speed with a time-to-first-token of 15630ms using Q4_K_M quantization.
For coding workloads, HelpingAI2 9B i1 on MacBook Pro M3 24GB receives a C grade with 12.4 tok/s and 126K context.
On MacBook Pro M3 24GB, HelpingAI2 9B i1 can safely use up to 126K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Not always. MacBook Pro M3 24GB can often fit larger models thanks to unified memory, but a discrete GPU with dedicated high-bandwidth VRAM may still decode faster once the model fits. For this combination, the important distinction is capacity versus sustained throughput.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/hf-mradermacher--helpingai2-9b-i1-gguf-on-m3-24gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
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